Normative modeling of brain white matter microstructure using diffusion tensor metrics in 52,719 participants

Motivation: It is currently difficult to compute normative models for diffusion MRI metrics of the brain’s white matter across the lifespan due to scanner/protocol effects that are hard to eliminate during harmonization. Goal(s): We set out to build large-scale multi-site normative models for DTI metrics of the white matter of the human brain. Approach: Hierarchical Bayesian Regression was run on ROI metrics derived using the ENIGMA-DTI protocol to determine the age trajectory and centile curves of DTI metrics. Results: We built DTI reference models based on 52,719 subjects that allowed us to detect deviations from the norm for patients with brain diseases. Impact: These reference models are valuable for detecting microstructural deviations from the normal range, while modeling scanner, protocol and cohort effects. They will be used in our ENIGMA consortium to map profiles of microstructural anomalies in >20 neurological and psychiatric conditions.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0140
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Normative modeling of brain white matter microstructure using diffusion tensor metrics in 52,719 participants

Neda Jahanshad, Clara Moreau, Elnaz Nourollahimoghadam, Emily Laltoo et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced Neuroimaging Techniques and Applications
article

Normative modeling of brain white matter microstructure using diffusion tensor metrics in 52,719 participants

Neda Jahanshad, Clara Moreau, Elnaz Nourollahimoghadam, Emily Laltoo, Yixue Feng, Sebastian Benavidez, Leila Nabulsi, Alyssa H. Zhu, Julio E. Villalón‐Reina, Tamoghna Chattopadhyay, Talia M. Nir, Katherine E. Lawrence, Paul M. Thompson, Sophia Thomopoulus
article en

Abstract

Motivation: It is currently difficult to compute normative models for diffusion MRI metrics of the brain’s white matter across the lifespan due to scanner/protocol effects that are hard to eliminate during harmonization. Goal(s): We set out to build large-scale multi-site normative models for DTI metrics of the white matter of the human brain. Approach: Hierarchical Bayesian Regression was run on ROI metrics derived using the ENIGMA-DTI protocol to determine the age trajectory and centile curves of DTI metrics. Results: We built DTI reference models based on 52,719 subjects that allowed us to detect deviations from the norm for patients with brain diseases. Impact: These reference models are valuable for detecting microstructural deviations from the normal range, while modeling scanner, protocol and cohort effects. They will be used in our ENIGMA consortium to map profiles of microstructural anomalies in >20 neurological and psychiatric conditions.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Advanced Neuroimaging Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.